Grant: Discovery Grants Program - Individual

Industry 4.0 and automation are fundamentally transforming the entire manufacturing process life cycle through extensive digitalization and the integration of large data collections. However, a significant challenge lies in effectively implementing these novel methods and technologies within the manufacturing environment. Presently, most of the manufacturing challenges turn around process design, process control, and process optimization. To comprehensively understand and simulate composite manufacturing processes, the key lies in employing accurate and dependable modeling techniques. At present, the modeling approach related to different manufacturing processes such as automated fiber placement (AFP) relies on physics-based models (PBM) to gain insights into structural design and its translation into a manufacturing plan. In parallel, data-driven models (DDM) can be employed to better understand and utilize manufacturing and inspection related data. This proposal will be implemented by using both PBM and DDM approaches. The first approach is based on the PBM. The primary objective is to model automated composite manufacturing processes, such as automated fiber placement (AFP), by using physical concepts, engineering principles, mathematical and numerical methods. The focus will be on enhancing the AFP process models to achieve more precise predictions concerning defect formation during automated fiber placement process. the second approach is to use DDM methods to effectively monitor, adapt, and optimize the complex composite manufacturing process. These complementary modeling methods together pave the way for efficient and innovative advancements in the field of automated and smart manufacturing.